Catchment scale hydrological modelling: A review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications

Pechlivanidis, Ilias; Jackson, Bethanna; McIntyre, Neil; Wheater, H. S.

doi:10.30955/gnj.000778

Cited by 71 publications

(19 citation statements)

References 140 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, total uncertainty is defined as the accumulation of all sources of uncertainty considered in a study; this definition is used in this study as well. Separating the contribution of each source of uncertainty is difficult due to the complex interactions among sources; sampling techniques are generally applied to consider each source of uncertainty after propagation to output (Pechlivanidis et al, 2011). For instance, models like WATFLOOD (Kouwen, 2018) use grouped response units, an approach that ties parameter values (parameter uncertainty) to landcover (input uncertainty) (e.g.…”

Section: Cumulative Effects Of Uncertainty On Simulated Streamflow Inmentioning

confidence: 99%

Cumulative Effects of Uncertainty on Simulated Streamflow in a Hydrologic Modeling Environment

Pokorny

Stadnyk²,

Ali³

et al. 2021

Elem Sci Anth

View full text Add to dashboard Cite

It is common in the literature to not consider all sources of uncertainty simultaneously: input, structural, parameter, and observed calibration data uncertainty, particularly in data-sparse environments due to data limitations and the complexities that arise from data limitations when propagating uncertainty downstream in a modelling chain. This paper presents results for the propagation of multiple sources of uncertainty towards the estimation of streamflow uncertainty in a data-sparse environment. Uncertainty sources are separated to ensure low likelihood uncertainty distribution tails are not rejected to examine the interaction of sources of uncertainty. Three daily resolution hydrologic models (HYPE, WATFLOOD, and HEC-HMS), forced with three precipitation ensemble realizations, generated from five gridded climate datasets, for the 1981–2010 period were used to examine the effects of cumulative propagation of uncertainty in the Lower Nelson River Basin as part of the BaySys project. Selected behavioral models produced an average range of Kling-Gupta Efficiency scores of 0.79–0.68. Two alternative methods for behavioral model selection were also considered that ingest streamflow uncertainty. Structural and parameter uncertainty was found to be insufficient, individually, by producing some uncertainty envelopes narrower than observed streamflow uncertainty. Combined structural and parameter uncertainty, propagated to simulated streamflow, often enveloped nearly 100% of observed streamflow values, however, high and low flow years were generally a source for lower reliabilities in simulated results. Including all sources of uncertainty generated simulated uncertainty bounds that enveloped most of the observed flow uncertainty bounds including improvement for high and low flow years across all gauges although the uncertainty bounds generated were of low likelihood. Overall, accounting for each source of uncertainty added value to the simulated uncertainty bounds when compared to hydrometric uncertainty; the inclusion of hydrometric uncertainty was key for identifying the improvements to simulated ensembles.

show abstract

Section: Cumulative Effects Of Uncertainty On Simulated Streamflow Inmentioning

confidence: 99%

Cumulative Effects of Uncertainty on Simulated Streamflow in a Hydrologic Modeling Environment

Pokorny

Stadnyk²,

Ali³

et al. 2021

Elem Sci Anth

View full text Add to dashboard Cite

show abstract

“…General models of circulation (GCMs) enable threedimensional simulations under rising greenhouse gas concentration scenarios of the earth's climate system and are excellent tools for quantitative comprehension of climate dynamics (Raneesh & Santosh, 2011;Xu et al, 2020). Evaluation of the hydrological effects of CC includes merging hydrological models with GCM outputs (Pechlivanidis et al, 2011;Wang et al, 2020). Pre-processing in terms of bias correction is required to ensure that GCMs are reasonably accurate and reproduce the future climate to remove the biases in the data Mudbhatkal & Mahesha, 2018a).…”

Section: Introductionmentioning

confidence: 99%

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

Sharannya

Venkatesh

Mudbhatkal

et al. 2021

Environ Monit Assess

View full text Add to dashboard Cite

CC on regional hydrology, a scenario approach was adopted and three scenarios depicting near-future (2006-2040), mid-future (2041-2070), and farfuture (2071-2100) based on climate were established. The present rate of LU change indicated a reduction in forest cover by 20% and an increase in urbanized areas by 9.5% between 1988 and 2016. It was estimated that forest cover in the catchments may be expected to halve compared to the presentday LU (55% in 2016 to 23% in 2075), along with large-scale conversion to agricultural lands (13.5% in 2016 to 49.5% in 2075). As a result of changes to LU and forecasted climate, it was found that rivers in the Western Ghats of India might face scarcity of fresh water in the next two decades until the year 2040. However, because of large-scale LU conversion toward the year 2050, streamflow in rivers might increase as high as 70.94% at certain times of the year. Although an increase in streamflow is perceived favorable, the streamflow changes during summer and winter may be expected to affect the cropping calendar and crop yield. The changes to streamflow were also linked to a 4.2% increase in ecologically sensitive wetlands of the Aghanashini river catchment.

show abstract

“…2 of 21 catchment for a given period of interest to provide a good 'fit' of the data. This should ensure that the model components and parameters retain their original physical meaning or interpretation [15,16].While some catchment and model pairings can achieve good calibration performance, there are always some catchments that do not perform well relative to other catchments for example, References [17] (Figure 12), [18] ( Figure 6). Even if good calibration performance is obtained, models frequently fail when tested in a new period outside which it has been trained.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

Conceptual Models and Calibration Performance—Investigating Catchment Bias

Buzacott

Tran

Ogtrop

et al. 2019

Water

View full text Add to dashboard Cite

Many lumped rainfall-runoff models are available but no single model can account for the uniqueness and variability of all catchments. While there has been progress in developing frameworks for optimal model selection, the process currently selects a range of model structures a priori rather than starting from the hydrological data and processes. In addition, studies on differential split sample tests (DSSTs) have focused on objective function definitions and calibration approaches. In this study, seven hydrological signatures and 12 catchment characteristics from 108 catchments around Australia were extracted for two 7-year time periods: (1) wet and (2) dry. The data was modelled using the GR4J, HBV and SIMHYD models using three objective functions to explore the relationship between model performance, catchment features and identified parameters. The hypothesis is that the hydrological signatures and catchment characteristics reflect catchment behaviour, and that certain signatures and characteristics are associated with better calibration performance. The results show that a greater percentage of catchments achieved a better calibration performance in the wet period compared to the dry period and that better calibration performance is associated with catchments that have greater cumulative flow and a steeper flow duration curve. The findings are consistent across the three models and three objective functions, suggesting that there is a bias in the studied models to wetter catchments. This study echoes the need to develop a conceptual model that can accommodate a wide variety of catchments and climates and provides a foundation to optimise and improve model selection in catchments based on their unique characteristics.

show abstract

Catchment scale hydrological modelling: A review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications

Cited by 71 publications

References 140 publications

Cumulative Effects of Uncertainty on Simulated Streamflow in a Hydrologic Modeling Environment

Cumulative Effects of Uncertainty on Simulated Streamflow in a Hydrologic Modeling Environment

Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India

Conceptual Models and Calibration Performance—Investigating Catchment Bias

Contact Info

Product

Resources

About